Context-aware recommendation method with content-based filtering for video data management

2022;
: pp. 1 - 11
1
Lviv Polytechnic National University, Computer Engineering Department

The problem of context-aware recommendation with content filtering for managing video data as part of an online video hosting platform has been considered. Approaches to creating online video hosting platforms with recommendation of video data have been considered. A comparison of popular online video hosting platforms is given.

A method of context-aware recommendation of video data with content-based filtering is proposed, which involves saving information about the user’s interaction with the video, obtaining and saving information about which videos the user liked, determining the user’s context, forming a profile of user preferences, forming a profile of user preferences depending on context, determining the similarity of the video profile to the profile of the user’s preferences (with and without taking into account the context), determining the relevance of the video to the context, and the final determination of the relevance of the video to the user’s preferences based on the proposed summary indicator of relevance.

The developed structure of the online video hosting platform with context-aware recommendation of video data is given. The algorithm of its work is considered. The database structure of the online video hosting platform with context-aware recommendation of video data is proposed.

  1. Lee J. (2005). Scalable Continuous Media Streaming Systems: Architecture, Design, Analysis and Implementation. Wiley. – 394 p. ISBN: 978-0-47-085754-0.
  2. Ce Z., Yuenan L., Xiamu N. (2010). Streaming Media Architectures, Techniques, and Applications: Recent Advances. IGI Global. – 502 p. ISBN: 978-1-61-692831-5.
  3. Fangming L. (2011). Large-scale peer-assisted online hosting, distribution and video streaming systems: design, modeling and practice, Ph.D. Thesis, Computer Science and Engineering. – 143 p. DOI: 10.14711/thesis- b1136551.
  4. Parthasarathy Ranganathan et al. (2022). Warehouse-Scale Video Acceleration, IEEE Micro. Vol. 42. No. 4. Pp. 18–26. DOI: 10.1109/MM.2022.3163244.
  5. Xu C., Haitao L., Jiangchuan L. (2013). Video sharing propagation in social networks: Measurement, modeling, and analysis. In: Proceedings IEEE INFOCOM, 2013. Pp. 45–49. DOI: 10.1109/INFCOM.2013.6566732.
  6. Davidson J., Liebald B., Liu J. and Nandy P. (2010). The YouTube video recommendation system. In: Proceedings of the 2010 ACM Conference on Recommender Systems, RecSys 2010. Barcelona, Spain. Pp. 293–296. DOI: 10.1145/1864708.1864770.
  7. Zhe Zhao et al. (2019). Recommending what video to watch next: a multitask ranking system. In: Proceedings of the 13th ACM Conference on Recommender Systems (RecSys’19). Pp. 43–51. DOI: 10.1145/3298689.3346997.
  8. Cheuque G., Guzmán J. and Parra D. (2019). Recommender Systems for Online Video Game Platforms: the Case of STEAM. In: Proceedings of The 2019 World Wide Web Conference. Pp. 763–771. DOI: 10.1145/3308560.3316457.
  9. Ricci F., Rokach L., Shapira B. (eds.). (2022). Recommender Systems Handbook. 3rd ed., Springer. – 1060 p. ISBN: 978-1-0716-2196-7.
  10. Aggarwal C. (2016). Recommender Systems: The Textbook. Springer. – 519 p. ISBN: 978-3-19-29657-9.
  11. Schrage M. (2020). Recommendation Engines. The MIT Press. – 296 p. ISBN: 978-0-26-253907-4.
  12. Falk K. (2019). Practical Recommender Systems. Manning Publications. – 432 p. ISBN: 978-1-61-729270-5.
  13. Robillard M., Maalej W., Walker R. and Zimmermann T. (eds.). (2014). Recommendation Systems in Software Engineering. Springer-Verlag Berlin Heidelberg. – 560 p. ISBN: 978-3-66-252404-6.
  14. Jannach D. (2010). Recommender Systems: An Introduction. Cambridge University Press. – 352 p. ISBN: 978-0-52-149336-9.
  15. Jie L., Qian Z., Guangquan Z. (2020). Recommender Systems: Advanced Developments. WSPC. – 362 p. ISBN: 978-9-81-122462-1.
  16. Suresh K. G. (2017). Building Recommendation Engines. Packt Publishing. – 357 p. ISBN: 978-1-78- 588485-6.
  17. Neumann A. (2009). Recommender Systems for Information Providers: Designing Customer Centric Paths to Information. Physica-Verlag Heidelberg. – 158 p. ISBN: 978-3-79-082578-7.
  18. Isinkayea F., Folajimib Y. and Ojokohc B. (2015). Recommendation systems: Principles, methods and evaluation. Egyptian Informatics Journal, Volume 16, Issue 3, November, pp.261-273. DOI: 10.1016/j.eij.2015.06.005.
  19. Jie Lu, Dianshuang Wu, Mingsong Mao, Wei Wang and Guangquan Zhang (2015) Recommender system application developments: A survey. Decision Support Systems, Volume 74, p.12-32. DOI: 10.1016/j.dss.2015.03.008.
  20. Leskovec, J., Rajaraman, A., Ullman, J. (2020) Mining of Massive Datasets. 3rd ed. Cambridge University Press. – 565 p. ISBN: 978-1-10-847634-8.
  21. Connor R. (2016). A Tale of Four Metrics. In: Amsaleg L., Houle M., Schubert E. (eds.). Similarity Search and Applications. SISAP 2016. Lecture Notes in Computer Science. Vol. 9939. Springer. Pp. 210-217. DOI: 10.1007/978-3-319-46759-7_16.
  22. Schilit B., Adams N. and Want R. (1994). Context-aware computing applications. In: Proceedings of the IEEE Workshop on “Mobile Computing Systems and Applications”. IEEE Computer Society. Pp. 85–90. DOI: 10.1109/wmcsa.1994.16.
  23. Abowd G., Dey A., Brown P., Davies N., Smith M. and Steggles P. (1999). Towards a Better Under- standing of Context and Context-Awareness. In: Gellersen H. (ed.). Handheld and Ubiquitous Computing. Lecture Notes in Computer Science. Vol. 1707. Springer, Berlin, Heidelberg. – Pp. 304-307. DOI: 10.1007/3-540-48157- 5_29.
  24. Bolchini C., Curino C., Quintarelli E., Schreiber F. and Tanca L. (2007). A data-oriented survey of context models. ACM SIGMOD Record, 36, 4. Pp. 19–26. DOI: 10.1145/1361348.1361353.
  25. Perera C., Zaslavsky A., Christen P. and Georgakopoulos D. (2014). Context Aware Computing for The Internet of Things: A Survey. IEEE Communications Surveys & Tutorials. Vol. 16. No. 1. First Quarter, pp. 414–454. DOI: 10.1109/surv.2013.042313.00197.
  26. Grifoni P., D’Ulizia A. and Ferri F. (2018). Context-Awareness in Location Based Services in the Big Data Era, In: Skourletopoulos G., Mastorakis G., Mavromoustakis C., Dobre C. and Pallis E. (eds.). Mobile Big Data. Lecture Notes on Data Engineering and Communications Technologies, Springer. Vol. 10. Pp. 85–127. DOI: 10.1007/978-3-319-67925-9_5.
  27. Capurso N., Bo M., Tianyi S. and Xiuzhen C. (2018). A survey on key fields of context awareness for mobile devices. Journal of Network and Computer Applications. Vol. 118. Pp. 44–60. DOI: 10.1016/j.jnca.2018.05.006.
  28. Botchkaryov A. (2018) Context-Aware Task Sequence Planning for Autonomous Intelligent Systems. Advances in Cyber-Physical Systems, Lviv. Vol. 3. No. 2. Pp. 60–66. DOI: 10.23939/acps2018.02.060.
  29. Adomavicius G. and Tuzhilin A. (2011). Context-Aware Recommender Systems. In: Recommender Systems Handbook, ed. by Francesco Ricci et al., Springer. Pp. 217–253. DOI: 10.1007/978-0-387-85820-3_7.
  30. Adomavicius G., Mobasher B., Ricci F. and Tuzhilin A. (2011). Context-Aware Recommender Systems. Ai Magazine, 32(3). Pp. 67–80. DOI: 10.1609/aimag.v32i3.2364.
  31. Abbar S., Bouzeghoub M., Lopez S. (2009). Context-Aware Recommender Systems: A Service-Oriented Approach. In: Proceedings of the 3rd International Workshop on Personalized Access, Profile Management and Context Awareness in Databases (PersDB). Lyon, France. Available at: http://persdb09.stanford.edu/ proceedings/persdb-6.pdf (accessed: 29 September 2022).
  32. Shaina R. and Chen D. (2019). Progress in context-aware recommender systems: An overview. Computer Science Review. Vol. 31. Pp. 84–97. DOI: 10.1016/j.cosrev.2019.01.001.
  33. Nawrocki P., Śnieżyński B. and Czyżewski J. (2017). Learning Agent for a Service-Oriented Context- Aware Recommender System in a Heterogeneous Environment, Computing and Informatics. Vol. 35(5). Pp. 1005– 1026. Available at: https://www.cai.sk/ojs/index.php/cai/article/view/3354 (Accessed: 29 September 2022).
  34. Bouneffouf D., Bouzeghoub A., Gancarski A. (2012). Following the User’s Interests in Mobile Context- Aware Recommender Systems: The Hybrid-e-greedy Algorithm. In: Proceedings of the 2012 26th International Conference on Advanced Information Networking and Applications Workshops, Lecture Notes in Computer Science, IEEE Computer Society. Pp. 657–662. DOI: 10.1109/waina.2012.200.